A high-quality ultrasmooth surface is demanded in short-wave optical systems. However, the existing polishing methods have difficulties meeting the requirement on spherical or aspheric surfaces. As a new kind of small tool polishing method, active feed polishing (AFP) could attain a surface roughness of less than 0.3 nm (RMS) on spherical elements, although AFP may magnify the residual figure error or mid-frequency error. The purpose of this work is to propose an effective algorithm to realize uniform removal of the surface in the processing. At first, the principle of the AFP and the mechanism of the polishing machine are introduced. In order to maintain the processed figure error, a variable pitch spiral path planning algorithm and the dwell time-solving model are proposed. For suppressing the possible mid-frequency error, the uniformity of the synthesis tool path, which is generated by an arbitrary point at the polishing tool bottom, is analyzed and evaluated, and the angular velocity ratio of the tool spinning motion to the revolution motion is optimized. Finally, an experiment is conducted on a convex spherical surface and an ultrasmooth surface is finally acquired. In conclusion, a high-quality ultrasmooth surface can be successfully obtained with little degradation of the figure and mid-frequency errors by the algorithm.
In order to realize removal function model building with high precision in computer controlled optical polishing process, a kind of new algorithm is proposed in this paper, which was based on edge detection technology. The algorithm preprocesses the interferometric metrology result and inverts it into digital image data at first. And then the Canny edge detecting operator and Hough transform are used to detect and extract the image edge. At last the boundary of the removal function was acquired. The center of removal function boundary was allocated quickly in this algorithm, which was assisted with the particularly center position. It reduced a lot of blind calculation for the target center of circle accumulating counts. At last, the experiment result indicated the algorithm in this paper could be used in removal function modeling and dwell time optimization solving.
Micro fluid-jet polishing technology is a new kind of ultra-smooth machining method which proposed on the basis of
float polishing principle and combined with small tool polishing. This article will use micro jet ultra-smooth machine
which developed by ourselves to develop the process experiment for plane elements. In the experiment, the material
removal rate and surface roughness are taken as the assessment index, and the orthogonal experiment method is used to
study the processing effect of different process parameters, such as the spindle speed, the pressure of the grinding head
and the abrasive concentration. On the basis of the experimental results and combined with the micro jet polishing
mechanism, the processing effect law of the various process parameters is analyzed. It shows that, the influence of
polishing pressure and abrasive concentration on the removal efficiency is single, that is to say, the removal efficiency
can be increased either by increasing the polishing pressure or by increasing the concentration of the slurry. However,
the influence of the grinding speed on removal efficiency is not simple, the removal efficiency can be increased by
increasing the grinding speed in the certain range, if continue to increase, the removal efficiency will decrease. The
influence of the process parameters on the roughness is more complex, but it can be summarized grossly as follows: if
the roughness is required to reduce quickly, the large polishing pressure and high concentration slurry can be chosen,
but it has a large depth of removal; if the roughness is required to reduce and the removal depth is as small as possible,
the little polishing pressure and the dilute polishing liquid can be chosen, but it has a long polishing time. So in the
actual processing, the process parameters should be adjusted according to different machining needs, to finally reach the
optimization.
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